The technology described herein relates to a method of determining the influence of a given variable in a phenomenon.
Detecting patterns that relate to particular diseases or failure modes in machines or observed events can be very challenging. It is generally easier to determine when symptoms (or measurements) are abnormal. Knowing that a situation is abnormal can be quite valuable. However, there is even more value if the abnormality can be tagged with a severity rating and/or associated with a specific condition or failure mode. Diagnostic information is contained in the pattern of association between input variables (e.g. measurement parameters) and anomaly. However, this pattern can be very difficult to extract.
Within the process industry, Principal Component Analysis (PCA) is often used for anomaly detection or fault diagnosis. Variable contributions to the residual or principal components can be calculated. This method provides an indication of which variables contribute most to the measure of abnormality. However, PCA has restrictions. It is uni-modal, meaning that its utility is limited when data are generated from complex densities and it does not provide an intuitive method for handling missing data.
Another approach for detecting the contribution of variables is to calculate residuals. For a specific variable, a regression technique is used to predict the variable's value which is then subtracted from the measured value to derive the residual. The magnitude of the residual provides a measure of its contribution to an anomalous state. However, it can still be difficult to directly compare different variables. And, if multiple variables are contributing to the anomaly, the outputs from the residuals can be misleading. The regression technique is often uni-modal and will suffer similar restrictions to PCA.